Looking to exploit the stock market? The key to making millions may be as close as your Twitter feed.
In a new paper, researchers at Indiana University, Bloomington's School of Informatics and Computing claim to have found the correlation between the value of the Dow Jones Industrial Average and public sentiment as measured through Twitter. PhysOrg reports that Associate Professor Johan Bollen and Ph.D. candidate Huina Mao were able to use millions of tweets to generate an algorithm that effectively predicts the impact of changes in the public attitude toward the economy on the performance of the stock market.
Students of history know well that financial panics can be self-fulfilling prophecies, with investors' panic over a perceived crisis leading to an actual crash. But past researchers have concluded that the influence of news events or major crises, while obviously connected to the behavior of the stock market, is essentially unpredictable given the difficulty of efficiently measuring their impact on public attitudes toward the economy in some sort of consistent index. In their paper, Bollen and Mao contend that early indicators of economic behavior could be extracted from social media outlets like Twitter, the mediums closest to real-time changes in the public mood. As Bollen and Mao write, large surveys of the public mood from representative samples -- like a Gallup poll or other consumer indices -- are too expensive or time-consuming to conduct, while more informal gauges of moods (like asking a group of people on the street) are generally inaccurate. A tweet, on the other hand, is a perfect unit for measuring changes in the public mood; confined to 140 character morsels and already subject to aggregation by various social media software, Twitter feeds are an easily mined source of public sentiment.